1. Field of the Invention
The present invention relates to a pattern inspection device and particularly relates to a pattern inspection method and a pattern inspection device characterized by its reference image preparation procedure to prepare the reference image for Die-To-Data Base Inspection from the design data.
2. Description of the Related Art
It is generally necessary to inspect fine patterns of the semiconductor integrated circuit or the like formed on the photo-mask, reticle or wafer liquid crystal using a pattern inspection device to see whether they are correctly drawn according to the dimensions and shapes of the ideal pattern prepared from the design data.
In this type of pattern inspection device, the design data given with square or trapezoid position coordinates and line length are expressed as binary bit string consisting of 0 and 1 corresponding to the inspected pattern. Density values in a pixel are generated by integration of the bit string within the pixel width of a predetermined resolution.
Next, a certain optical point spread function is prepared based on the pattern information obtained by scanning the inspected pattern with the laser beam and making an image on the light receiving device with the light which has passed the pattern or based on the edge profiling of the pattern image obtained from the image sensing system. Using this function and executing the convolution operation with the multiple gray scale data (multi-value data) in the design data, the reference image is obtained.
Defects on the inspected pattern are detected by reading the reference image obtained from the design data synchronizing with the image of the inspected pattern obtained by optical scanning or by input from the image sensing system and by finding discrepancies between the image of the inspected pattern and the reference image at corresponding pixel positions (Die-To-Data BASE inspection).
Under the optical conditions and influence of the manufacturing processes, the image of the inspected pattern has thicker or thinner line widths and other dimensional errors when compared with the ideal design values. Pseudo discrepancies from the reference image obtained from the design data tend to result in pseudo defects, which should not be judged as defects.
Therefore, it is necessary to execute appropriate characteristics extraction such as edge position detection corresponding to the characteristics amount in each inspection zone and to correct in advance the pattern width of the multiple gray scale image in the design data before convolution processing with the optical point spread function so that such width comes to the edge position of the image in the inspected pattern.
In the conventional reference image preparation method, when correcting the pattern width of the reference image, the inspected pattern image is subjected to an appropriate edge detection processing so that the pattern correction width in the design data are used for correction as it is, i.e. the binary data by the unit of bits (pixel).
In this case, by preparing the change bit pattern from the design bit pattern and obtaining its EX-OR with the bit data of the inspected pattern as the binary expression of the actual image, the pattern of the reference image is resized.
In addition, for edges and corners extracted from the design data by edge position detection and corner recognition, corrective templates by the unit of bits expressing an edge or corner of a certain pattern edge angle (0, 45, 90 and 135 deg. for example) are prepared.
Then, the correction template the most similar to the actual image of the edges and corners is selected for correction of the original design data. The corrected design data are formed into multi-value data again for reference image preparation. Defects are detected by judging whether the gray scale difference between the obtained reference image and the actual image is defective or not using the threshold of a proper defect algorithm (Refer to Japanese Patent Application Laid-Open No. 350776/1992, for example).
Conventionally, image processing methods have been often used to detect the edge position of the inspected pattern. Such methods using image processing can be largely classified into density methods, methods using primary differentiation of density and methods using secondary differentiation of density (Reference material: Yoshihiko Nomura et al. “Sub-pixel Expression of Edge Position Measurement And Error Analysis”. (Electronic Information Communication Association Paper D-11 Vol. J73-D-11 No. 9 pp. 1458–1467, September 1990).
According to a density method, a virtual edge pattern which can be applied the most suitably to the original image is determined for the primary to the third density moment so as to detect the edge position.
Density methods with primary differentiation include the method to consider the gravity center of the primary differentiation for all pixels forming the edge to be the edge position, the method to apply the primary differentiation of three consecutive pixels near the peak to the parabola so that the peak of the obtained regression curve is considered the edge position, and the method to apply the primary differentiation of density to the normal distribution by regression with least squares so that the extreme position of the regressive curve is considered the edge position. In the method using secondary differentiation of density, the secondary differentiation is determined using a special filter and the zero-cross position when the secondary differentiation result of two adjacent pixels with their positive and negative values transferred are linearly interpolated is considered to be the edge position.
A fine pattern drawn on the substrate such as photo mask has a zone of only about three pixels for transition from the black pixel level to the white pixel level at the pattern edge section and the edge profile is quite sharp. For this reason, when using the edge position detection method according to the conventional image processing, there is a drawback that a high level calculation is required even if the edge extraction algorithm by the unit of pixels or sub-pixels is used. Another drawback is that it takes a long time for calculation to identify the edge position by the unit of sub-pixels.
Further, in the conventional reference image preparation method, the relation between the optical point spread function corresponding to the beam spot strength including the machine system and the pattern edge position is not clear. Since the edge measurement position by the optical system and the edge extraction position by the image processing do not coincide, the pattern width of the inspected pattern and the pattern correction width of the reference data cannot be determined correctly.